Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements.
Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements.
复制标题
通过综合血液分子测量准确预测急性胰腺炎的严重程度
DOI：
10.18632/aging.202689
复制
发表时间：
2021-03-10
期刊：
影响因子：
--
通讯作者：
Shi KQ
中科院分区：
文献类型：
--
作者：
Sun HW;Lu JY;Weng YX;Chen H;He QY;Liu R;Li HP;Pan JY;Shi KQ
背景：重症急性胰腺炎（SAP）的早期诊断对于降低其死亡率和改善预后至关重要。我们的目标是根据常规临床测试结果开发准确且适用的机器学习预测模型，以对急性胰腺炎（AP）严重程度进行分层。结果：我们确定了 11 种预测 AP 严重程度的标记物，并训练了名为 APSAVE 的 AP 分层模型，该模型在 24 小时内以 0.74 +/- 0.04 的平均曲线下面积 (AUC) 对 AP 病例进行分类。在568个验证案例中进一步验证，AUC为0.73，与Ranson标准（AUC = 0.74）相似，高于APACHE II和BISAP（AUC分别为0.69和0.66）。结论：我们开发并验证了基于静脉血标记物的 AP 严重程度分层模型，该模型具有更高的准确性和更广泛的适用性，有望降低 SAP 死亡率并改善其临床结果。材料和方法：945 名 AP 患者参加了本研究。 AP 患者入院 24 小时内进行了涵盖 65 种生物标志物的临床静脉血检测。 SAP 预测模型是通过统计学习构建的，以选择最能预测 AP 严重程度的生物标志物。
Background: Early diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results for stratifying acute pancreatitis (AP) severity. Results: We identified 11 markers predictive of AP severity and trained an AP stratification model called APSAVE, which classified AP cases within 24 hours at an average area under the curve (AUC) of 0.74 +/- 0.04. It was further validated in 568 validation cases, achieving an AUC of 0.73, which is similar to that of Ranson’s criteria (AUC = 0.74) and higher than APACHE II and BISAP (AUC = 0.69 and 0.66, respectively). Conclusions: We developed and validated a venous blood marker-based AP severity stratification model with higher accuracy and broader applicability, which holds promises for reducing SAP mortality and improving its clinical outcomes. Materials and Methods: Nine hundred and forty-five AP patients were enrolled into this study. Clinical venous blood tests covering 65 biomarkers were performed on AP patients within 24 hours of admission. An SAP prediction model was built with statistical learning to select biomarkers that are most predictive for AP severity.
登录
查看更多内容
影响因子：
29.4
作者：
Petrov, Maxim S.;Shanbhag, Satyanarayan;Windsor, John A.
通讯作者：
Windsor, John A.
影响因子：
9.8
作者：
Bollen, Thomas L.;Singh, Vikesh K.;Mortele, Koenraad J.
通讯作者：
Mortele, Koenraad J.
影响因子：
9.8
作者：
Papachristou, Georgios I.;Muddana, Venkata;Whitcomb, David C.
通讯作者：
Whitcomb, David C.
DOI：
10.1038/s41395-018-0048-1
发表时间：
2018-05
期刊：
The American journal of gastroenterology
影响因子：
--
作者：
Buxbaum J;Quezada M;Chong B;Gupta N;Yu CY;Lane C;Da B;Leung K;Shulman I;Pandol S;Wu B
通讯作者：
Wu B
影响因子：
2.9
作者：
Radenkovic, Dejan;Bajec, Djordje;Gregoric, Pavle
通讯作者：
Gregoric, Pavle